Jan 4, 2015 · Abstract:Consider the regularized sparse minimization problem, which involves empirical sums of loss functions for n data points (each of ...
We prove that finding an O(nc1dc2)-optimal solution to the regularized sparse optimization problem is strongly NP-hard for any c1,c2∈[0,1) such that c1+c2<1.
Jun 19, 2017 · Abstract. Consider the regularized sparse minimization problem, which involves empirical sums of loss functions for n data points (each of ...
We prove that finding an O(nC1dC2)-optimal solution to the regularized sparse optimization problem is strongly NP-hard for any C1, C2 ∈ [0, 1) such that C1 + C ...
Dec 18, 2022 · Bibliographic details on Strong NP-Hardness for Sparse Optimization with Concave Penalty Functions.
It is shown that if the penalty function is concave but not linear in a neighborhood of zero, then the optimization problem is strongly NP-hard, ...
The hardness results apply to a broad class of loss functions and sparse penalties. They suggest that one cannot even approximately solve these three problems ...
Missing: Concave | Show results with:Concave
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We show that if the penalty function is concave but not linear, then the optimization problem is strongly NP-hard. This result answers the complexity of many ...
Abstract. In this paper, we consider three typical optimization problems with a convex loss function and a nonconvex sparse penalty or constraint.
Abstract. In this paper, we consider three typical optimization problems with a convex loss function and a nonconvex sparse penalty or constraint.